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| 계층적 변분 추론× | 계층적 베이즈 추론× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 2016 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| 창시자≠ | Ranganath, Altosaar, Tran & Blei | Lindley & Smith; Gelman et al. |
| 유형≠ | Bayesian approximate inference | Bayesian multilevel model |
| 원전≠ | Ranganath, R., Altosaar, J., Tran, D. & Blei, D. M. (2016). Hierarchical Variational Models. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), PMLR 48, 324-333. link ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| 별칭 | HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| 관련≠ | 5 | 6 |
| 요약≠ | Hierarchical variational inference (HVI) extends standard variational inference by placing a richer, hierarchical structure on the variational family itself. Instead of using a simple mean-field approximation, HVI introduces auxiliary latent variables that capture dependencies among the main latent variables, yielding tighter evidence lower bounds and more accurate posterior approximations for complex Bayesian models. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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